Audio-visual emotion recognition in adult attachment interview

  • Authors:
  • Zhihong Zeng;Yuxiao Hu;Yun Fu;Thomas S. Huang;Glenn I. Roisman;Zhen Wen

  • Affiliations:
  • University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;IBM T.J.Watson Research Center;IBM T.J.Watson Research Center

  • Venue:
  • Proceedings of the 8th international conference on Multimodal interfaces
  • Year:
  • 2006

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Abstract

Automatic multimodal recognition of spontaneous affective expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting - Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression be at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System Emotion Codes. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AMHMM) in which Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in the preliminary AAI spontaneous emotion recognition experiments.